Type 2 diabetes is now considered one of the main threats to human health in the 21st century. In this project, we propose to use an integrative genomics approach to identify potentially functional regulatory variants in novel candidate genes for diabetes risk, in a large family based study. These objectively chosen candidate genes were obtained using large-scale transcriptional profiling of lymphocyte samples from 1,240 San Antonio Family Heart Study participants. Target candidate loci were nominated based on the existence of significant correlations of quantitative gene expression levels with fasting glucose levels, a major diabetes risk factor, plus one or more diabetes risk factors including fasting insulin and composite diabetes risk index. Using our unique family-based resource of quantitative genome-wide transcriptional profiles, we will examine 100 novel candidate genes that exhibit both strong evidence for cis-regulation of expression levels and significant correlations between expression levels and diabetes risk phenotypes. Our prior linkage-based evidence for cis-acting sequence variation can be exploited as a probabilistic causal anchor that should maximize our chance for finding functional variation within proximal promoters. For each of these objectively chosen genes, we will: 1) resequence approximately two kilobases of putative promoter region in 182 founder individuals to identify promoter variants;2) genotype all detected promoter variation in each of the 100 candidate genes in the 1,240 SAFHS samples for whom we have transcriptional profiles;3) test whether promoter sequence variants are associated with gene expression levels of the appropriate candidate gene;4) test for associations between promoter sequence variants and diabetes risk phenotypes;and 5) confirm observed associations in two independent sample populations. The estimated economic burden of diabetes in the United States alone is approximately $100 billion per year, making this disease of major public health importance. The knowledge gained by this project will help contribute to the understanding of the genetics of type 2 diabetes through the identification of novel and potentially functional candidate genes, assisting in the development of new preventative measures and/or therapies.